Speed versus quality in low bit-rate still image compression

This paper presents a fast and modified version of the embedded zero-tree wavelet (EZW) coding algorithm that is based on [4,5,15,20] for low bit-rate still image compression applications. The paper presents the trade-off between the image compression algorithm speed and the reconstructed image quality measured in terms of PSNR. The measurements show a performance speedup by a factor of 6 in average over the EZW [20] and the algorithm presented in [5]. This speedup causes a PSNR degradation of the reconstructed image by 0.8-1.8 dB in average. Nevertheless, the reconstructed image looks 'fine' with no particular visible artifacts even if we have an average degradation of 1 dB in PSNR. The fast algorithm with its achieved speedup is based on consecutive application of three different techniques: (1) Geometric multiresolution wavelet decomposition [15]. It contributes a speedup factor of 4 in comparison to the symmetric wavelet decomposition in [5,20]. It has a prefect reconstruction property which does not degrade the quality. (2) Modified and reduced version of the EZW algorithm for zero-tree coefficient classification. It contributes a speedup factor of 6 in comparison to the full processing in [5,20]. It degrades the quality of the reconstructed image. This includes efficient multiresolution data representation which enables fast and efficient traversing of the zero-tree. (3) Exact model coding of the zero-tree coefficients. It contributes a speedup factor if 15 in comparison to the adaptive modeling in [5]. This is a lossless step which reduces the compression rate because its entropy coder is sub-optimal. The paper presents a detailed description of the above algorithms accompanied by extensive performance analysis. It shows how each component contributes to the overall speed up. The fast compression has an option of manual allocation of compression bits which enables a better reconstruction quality at a pre-selected 'zoom' area.